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Autophagy, an evolutionarily conserved process for the bulk degradation of cytoplasmic components, serves as a cell survival mechanism in starving cells 1 , 2 . Although altered autophagy has been observed in various heart diseases, including cardiac hypertrophy 3 , 4 and heart failure 5 , 6 , it remains unclear whether autophagy plays a beneficial or detrimental role in the heart. Here, we report that the cardiac-specific loss of autophagy causes cardiomyopathy in mice. In adult mice, temporally controlled cardiac-specific deficiency of Atg5 (autophagy-related 5), a protein required for autophagy, led to cardiac hypertrophy, left ventricular dilatation and contractile dysfunction, accompanied by increased levels of ubiquitination. Furthermore, Atg5-deficient hearts showed disorganized sarcomere structure and mitochondrial misalignment and aggregation. On the other hand, cardiac-specific deficiency of Atg5 early in cardiogenesis showed no such cardiac phenotypes under baseline conditions, but developed cardiac dysfunction and left ventricular dilatation one week after treatment with pressure overload. These results indicate that constitutive autophagy in the heart under baseline conditions is a homeostatic mechanism for maintaining cardiomyocyte size and global cardiac structure and function, and that upregulation of autophagy in failing hearts is an adaptive response for protecting cells from hemodynamic stress.

With the aging population, the majority of patients in acute care hospitals in high-income countries are older adults. To identify the factors associated with pressure ulcers among patients aged ≥ 75 years in an acute care hospital,we conducted a retrospective study using individual-level secondary data obtained from patient medical and nursing records. The study population was patients aged ≥ 75 years who were admitted to the hospital between March 2016 and February 2021. We fitted multivariable logistic regression models. A total of 15,258 were included in the analysis, and 513 (3.4%) patients were with pressure ulcers. The factors associated with pressure ulcers were Body Mass Index (adjusted OR = 0.96, 95% CI: 0.93–0.98); albumin level (0.51, 0.42–0.63); cardiovascular disease (2.10, 1.57–2.82); infection (1.91, 1.07–3.42); bedridden (1.85, 1.01–3.39); using bed when moving (0.45, 0.25–0.84); fecal or urine incontinence (2.44, 1.46–4.06); admission to the emergency medicine (1.71, 1.06–2.74); length of stay (1.02, 1.01–1.02); and tube feeding (2.02, 1.44–2.82). Low nutritional status, low activity levels, incontinence, cardiovascular disease, infection, admission to the emergency medicine, and more extended hospital stay were significantly associated with pressure ulcers. Therefore, nurses in acute care should carefully monitor the patients aged ≥ 75 years with these factors.

Identifying patients who would benefit from extensive catheter ablation along with pulmonary vein isolation (PVI) among those with persistent atrial fibrillation (AF) has been a subject of controversy. The objective of this study was to apply uplift modeling, a machine learning method for analyzing individual causal effect, to identify such patients in the EARNEST-PVI trial, a randomized trial in patients with persistent AF. We developed 16 uplift models using different machine learning algorithms, and determined that the best performing model was adaptive boosting using Qini coefficients. The optimal uplift score threshold was 0.0124. Among patients with an uplift score ≥ 0.0124, those who underwent extensive catheter ablation (PVI-plus) showed a significantly lower recurrence rate of AF compared to those who received only PVI (PVI-alone) (HR 0.40; 95% CI 0.19–0.84; P -value = 0.015). In contrast, among patients with an uplift score < 0.0124, recurrence of AF did not significantly differ between PVI-plus and PVI-alone (HR 1.17; 95% CI 0.57–2.39; P -value = 0.661). By employing uplift modeling, we could effectively identify a subset of patients with persistent AF who would benefit from PVI-plus. This model could be valuable in stratifying patients with persistent AF who need extensive catheter ablation before the procedure.

Background:Radiology reports are usually written in a free-text format, which makes it challenging to reuse the reports.Objective:For secondary use, we developed a 2-stage deep learning system for extracting clinical information and converting it into a structured format.Methods:Our system mainly consists of 2 deep learning modules: entity extraction and relation extraction. For each module, state-of-the-art deep learning models were applied. We trained and evaluated the models using 1040 in-house Japanese computed tomography (CT) reports annotated by medical experts. We also evaluated the performance of the entire pipeline of our system. In addition, the ratio of annotated entities in the reports was measured to validate the coverage of the clinical information with our information model.Results:The microaveragedF1-scores of our best-performing model for entity extraction and relation extraction were 96.1% and 97.4%, respectively. The microaveragedF1-score of the 2-stage system, which is a measure of the performance of the entire pipeline of our system, was 91.9%. Our system showed encouraging results for the conversion of free-text radiology reports into a structured format. The coverage of clinical information in the reports was 96.2% (6595/6853).Conclusions:Our 2-stage deep system can extract clinical information from chest and abdomen CT reports accurately and comprehensively.

G-protein–coupled receptor (GPCR) agonists are well-known inducers of cardiac hypertrophy. We found that the shedding of heparin-binding epidermal growth factor (HB-EGF) resulting from metalloproteinase activation and subsequent transactivation of the epidermal growth factor receptor occurred when cardiomyocytes were stimulated by GPCR agonists, leading to cardiac hypertrophy. A new inhibitor of HB-EGF shedding, KB-R7785, blocked this signaling. We cloned a disintegrin and metalloprotease 12 (ADAM12) as a specific enzyme to shed HB-EGF in the heart and found that dominant-negative expression of ADAM12 abrogated this signaling. KB-R7785 bound directly to ADAM12, suggesting that inhibition of ADAM12 blocked the shedding of HB-EGF. In mice with cardiac hypertrophy, KB-R7785 inhibited the shedding of HB-EGF and attenuated hypertrophic changes. These data suggest that shedding of HB-EGF by ADAM12 plays an important role in cardiac hypertrophy, and that inhibition of HB-EGF shedding could be a potent therapeutic strategy for cardiac hypertrophy.

Abstract Background Endovascular treatment (EVT) is a well-established treatment for patients with chronic limb-threatening ischaemia, and below-the-knee (BTK) artery is its main target, although the re-intervention rate is still high. Understanding of the characteristics of BTK artery atherosclerosis would be required to overcome this issue. In this case series, we elucidated the characteristics of non-stenotic BTK artery atherosclerosis in the patients who received EVT of the superficial femoral artery (SFA) using optical frequency domain imaging (OFDI) and angioscopy. Case summary We presented five patients who underwent EVT of SFA and subsequent observation of ipsilateral BTK artery using OFDI and angioscopy. Patients one and two had advanced atherosclerosis; however, patients three, four, and five had only mild atherosclerosis. Discussion All patients had multiple risk factors for atherosclerosis and stenosis/occlusion of the SFA and ipsilateral BTK arteries. Furthermore, some patients had several other atherosclerotic vascular diseases suggesting the presence of advanced systemic atherosclerosis. On the other hand, some patients with multiple BTK artery stenosis/occlusion did not have advanced atherosclerosis in the examined BTK artery. The absence of significant atherosclerosis in a BTK artery in patients with multiple stenoses or occlusion in other ipsilateral BTK arteries may suggest some mechanism of vessel occlusion other than atherosclerosis. Further investigations are needed to clarify the mechanism.

Background:Documentation tasks comprise a large percentage of nurses’ workloads. Nursing records were partially based on a report from the patient. However, it is not a verbatim transcription of the patient's complaints but a type of medical record. Therefore, to reduce the time spent on nursing documentation, it is necessary to assist in the appropriate conversion or citation of patient reports to professional records. However, few studies have been conducted on systems for capturing patient reports in electronic medical records. In addition, there have been no reports on whether such a system reduces the time spent on nursing documentation.Objective:This study aims to develop a patient self-reporting system that appropriately converts data to nursing records and evaluate its effect on reducing the documenting burden for nurses.Methods:An electronic medical record–connected questionnaire and a preadmission nursing questionnaire were administered. The questionnaire responses entered by the patients were quoted in the patient profile for inpatient assessment in the nursing system. To clarify its efficacy, this study examined whether the use of the electronic questionnaire system saved the nurses’ time entering the patient profile admitted between August and December 2022. It also surveyed the usability of the electronic questionnaire between April and December 2022.Results:A total of 3111 (78%) patients reported that they answered the electronic medical questionnaire by themselves. Of them, 2715 (88%) felt it was easy to use and 2604 (85%) were willing to use it again. The electronic questionnaire was used in 1326 of 2425 admission cases (use group). The input time for the patient profile was significantly shorter in the use group than in the no-use group (P<.001). Stratified analyses showed that in the internal medicine wards and in patients with dependent activities of daily living, nurses took 13%-18% (1.3 to 2 minutes) less time to enter patient profiles within the use group (both P<.001), even though there was no difference in the amount of information. By contrast, in the surgical wards and in the patients with independent activities of daily living, there was no difference in the time to entry (P=.50 and P=.20, respectively), but there was a greater amount of information in the use group.Conclusions:The study developed and implemented a system in which self-reported patient data were captured in the hospital information network and quoted in the nursing system. This system contributes to improving the efficiency of nurses’ task recordings.

It is expected that a large amount of data related to diabetes and other chronic diseases will be generated. However, databases constructed without standardized data item sets can be limited in their usefulness. To address this, the Collaborative Committee of Clinical Informatization in Diabetes Mellitus was established in 2011 by the Japan Diabetes Society and Japan Association for Medical Informatics. The committee has developed core item sets and self‐management item sets for diabetes mellitus, hypertension, dyslipidemia, and chronic kidney disease in collaboration with the Japanese Society of Hypertension, Japan Atherosclerosis Society, Japanese Society of Nephrology, and Japanese Society of Laboratory Medicine, as well as a mapping table that aligns the self‐management item sets with the Japanese standardized codes for laboratory testing. The committee also determined detailed specifications for implementing the four self‐management item sets in personal health record (PHR) applications to facilitate risk stratification, the generation of alerts using information and communications technology systems, the avoidance of data input errors, and the generation of reminders to input the self‐management item set data. The approach developed by the committee may be useful for combining databases for various purposes (such as for clinical studies, patient education, and electronic medical record systems) and for facilitating collaboration between PHR administrators.

Left ventricular remodeling that occurs after myocardial infarction (MI) and pressure overload is generally accepted as a determinant of the clinical course of heart failure. The molecular mechanism of this process, however, remains to be elucidated. Apoptosis signal-regulating kinase 1 (ASK1) is a mitogen-activated protein kinase kinase kinase that plays an important role in stress-induced apoptosis. We used ASK1 knockout mice (ASK-/-) to test the hypothesis that ASK1 is involved in development of left ventricular remodeling. ASK-/-hearts showed no morphological or histological defects. Echocardiography and cardiac catheterization revealed normal global structure and function. Left ventricular structural and functional remodeling were determined 4 weeks after coronary artery ligation or thoracic transverse aortic constriction (TAC). ASK-/-had significantly smaller increases in left ventricular end-diastolic and end-systolic ventricular dimensions and smaller decreases in fractional shortening in both experimental models compared with WT mice. The number of terminal deoxynucleotidyl transferase biotin-dUDP nick end-labeling-positive myocytes after MI or TAC was decreased in ASK-/-compared with that in WT mice. Overexpression of a constitutively active mutant of ASK1 induced apoptosis in isolated rat neonatal cardiomyocytes, whereas neonatal ASK-/-cardiomyocytes were resistant to H2O 2-induced apoptosis. An in vitro kinase assay showed increased ASK1 activity in heart after MI or TAC in WT mice. Thus, ASK1 plays an important role in regulating left ventricular remodeling by promoting apoptosis.